Abstract Polarimetric Synthetic Aperture Radar (PolSAR) data, thanks to their specific
characteristics such as high resolution, weather and daylight independence, have become a …

Clustering algorithms with attribute weighting have gained much attention during the last
decade. However, they usually optimize a single-objective function that can be a limitation to …

Data clustering is an unsupervised learning task that has found many applications in various
scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a …

This paper presents kernel-based hard clustering methods with kernelization of the metric
and automatic weighting of the variables. The proposed methodology is supported by the …

Clustering has been used in various fields, such as image processing, data mining, pattern
recognition, and statistical analysis. Generally, clustering algorithms consider all variables …

The main objective of the Multiple Kernel k-Means (MKKM) algorithm is to extract non-linear
information and achieve optimal clustering by optimizing base kernel matrices. Current …

Clustering algorithms have attracted a lot of attentions recently in real-world applications.
However, the traditional clustering algorithms still have plenty of defects which are not yet …

Data clustering is an unsupervised learning task that has found many applications in various
scientific fields. The goal is to find subgroups of closely related data samples (clusters) in a …

Conventional Gaussian kernel c-means clustering algorithms are widely used in
applications. However, Gaussian kernel functions have an important parameter, the width …

Clustering is an essential technique that groups similar data points to uncover the
underlying structure and features of the data. Although traditional clustering methods such …